AlenkaF commented on code in PR #49843:
URL: https://github.com/apache/arrow/pull/49843#discussion_r3181661058
##########
docs/source/python/numpy.rst:
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@@ -73,3 +73,56 @@ representation as Arrow, and assuming the Arrow data has no
nulls.
For more complex data types, you have to use the
:meth:`~pyarrow.Array.to_pandas`
method (which will construct a Numpy array with Pandas semantics for, e.g.,
representation of null values).
+
+Timezone-aware Timestamps
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+NumPy's ``datetime64`` type does not support timezones. When converting a
+timezone-aware Arrow timestamp array to NumPy via
:meth:`~pyarrow.Array.to_numpy`,
+the timezone information is silently dropped:
+
+.. code-block:: python
+
+ >>> arr = pa.array([1735689600, 1735689600], type=pa.timestamp("s",
tz="UTC"))
+ >>> arr.type
+ TimestampType(timestamp[s, tz=UTC])
+ >>> arr.to_numpy()
+ array(['2025-01-01T00:00:00', '2025-01-01T00:00:00'],
+ dtype='datetime64[s]')
+
+If you need to preserve timezone information, there are two alternatives:
+
+* Convert to a Pandas Series, which supports timezone-aware ``datetime64``
dtypes:
+
+ .. code-block:: python
+
+ >>> arr.to_pandas()
+ 0 2025-01-01 00:00:00+00:00
+ 1 2025-01-01 00:00:00+00:00
+ dtype: datetime64[s, UTC]
+
+ To convert back to NumPy while preserving timezone information, use
+ ``timestamp_as_object=True`` to get an object array of Python ``datetime``
+ objects:
Review Comment:
```suggestion
To get a NumPy array while preserving timezone information, use
``timestamp_as_object=True``:
```
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